Information and its availability and accessibility to as many people as possible are instrumental to societal advancement. The advancement of the human civilization over the last 500-600 years can in a large part be attributed to the development of the Guttenberg press and the subsequent availability of printed books to the masses of Europe. Closer to home, the Revolutionary War and independence from Great Britain can in part be attributed to pamphleteers and their persuasive effect on a significant portion of the population.
The advent of the library making access to book and other written materials available to people of all social economic classes helped fuel technological development during the industrial revolution.
And as more and more information was created and became available to people, advancement in the means of organizing and providing access to information has advanced as well. In the last two decades the importance of libraries has begun to fade as the Internet has become a primary means of information retrieval and dissemination. Within the current decade, the advent of high speed wireless communications has literally put more information than has ever been stored in the largest libraries at a person's fingertips through laptop computers, PDAs and Smartphones no matter where the person is located.
Whereas, technological and societal advancement in the past was determined largely by the access to information and its availability to large portions of the population, the exponential growth of information has potentially created a new barrier: the difficulty of finding and locating particular pieces of reliable information concerning a topic or issue in the shear volume of information that may be available for the particular topic or issue. Often the hunt for information can be likened to the proverbial cliche of finding a needle in a haystack. In short, too much information can be too much of a good thing. In developed areas of the world, the focus has shifted from providing wholesale access to information to providing better means for accessing high quality and reliable information. The challenge is to organize the large volume of information available in such a way that what is not meaningful to each reader is filtered out in a customized fashion without the reader having to make too many decisions.
In the past decade or two, Knowledge-based Expert Systems have been developed that utilize a combination of rules and data to solve problems and relatively quickly disseminate specific high quality information to an interested person. Books and manuals require shifting through a significant amount of needless information to find the piece required by a person for a particular need. In contrast, Expert Systems based on contextual data provided to it quickly apply rules to filter the data and deliver the specific information required by a requestor. Or at least the foregoing is a promise offered by Expert Systems: a promise that has largely remained undelivered.
Widespread acceptance of expert systems has been hindered by several problems or issues. Unfortunately, Expert Systems can become very complex very quickly making the entry of quality information in a logical manner very difficult and time consuming as well as taxing the computational capabilities of all but the most powerful information systems. Referring to prior art FIG. 1, and outline of a simple decision tree 100 is provided. Decision trees are often utilized in simpler Expert Systems as a framework for the data and rules. Each box 110 represents a decision point wherein based on the resolution of a rule associated with the decision point, the Expert System moves to the next decision point/box by way of an associated link 120. As becomes clear from FIG. 1, the number of outcome permutations explode as the Expert System travels deeper into the tree. For a tree wherein each decision point has X possible choices, the number of outcome permutations for any particular decision tree is equal to X raised to the number of levels in the tree. For a simple tree having only two possible choices for any decision point: a five level tree has 32 possible outcomes; a ten level tree has 1024 outcomes; a twenty level tree has over one million outcomes; and a thirty level tree has nearly one billion outcomes. Furthermore, to solve complex problems or issues, large numbers of decision points and consequently levels are often required.
Obviously the quality of the information provided by an expert system is only as good as the person(s) providing the information: an expert must compile the data and rules (alone or working with computer professionals). While a person can immediately remember a chain of decisions a few levels back from any decision point within a tree, it is difficult if not impossible to recall all the decisions made to come to a decision point many levels deep in the tree. Accordingly, the process of populating a tree from a level deep within the tree often requires tracing back through the levels for each and every decision point at a particular level. Simply the process is very tedious and time consuming. In the end, producing complex trees having more than only a few levels becomes impractical.
Simply, decision trees and related Expert Systems map data in a manner that is very different from the way people think and reason. Rarely do people make decisions for each element of thought that pops into their heads. Rather, a person tends to think habitually following a path of thought linearly. Simply for a person, multitudes of decision points do not exist for each and every issue or conflict as they have been long resolved based on a multitude of life experiences. Over the years we refined our habits of thinking as paths of thought are regularly applied and tested to the questions we have to consider. Our habitual paths of thought become extremely reliable resources and we call upon them instinctively as established dispositions to confronting issues or solving problems. Occasionally, during a stream of consciousness when the human mind makes a decision it simply switches from a largely linear path to another linear path that is often related to the first. Furthermore, habitual paths of thought can merge or flow back into other habitual paths without conflict or inconsistency. Switching between paths of thought from one topic to another is seamless.
Decision trees and associated Expert Systems do not permit switching between topics or issues from within a tree. A decision tree can only be entered at its beginning at its first level and must be traversed all the way through before being exited. If a tree is left at some point midway through the tree to follow another tree concerning a related issue or concern, upon completion of the other tree, the first tree must be entered back at its beginning so that every decision made at decision points prior to the decision point of departure can be verified as true. Essentially, nesting more than one tree just creates a larger single tree further complicating the problems of the tree's size and unmanageability.
Because of the problems with decision trees and associated Expert Systems, they are not well suited to certain type of information dissemination tasks, such as making information more meaningful to a person. Simply, decision trees and Expert Systems are best suited to solving concrete problems with definite answers that logically flow together. Decision trees become very difficult to manage when there are too many nodes and when there are a large number of possible answers within the nodes that may have only a slight variation in their meaning. People tie together information in a multitude of ways that are both logical and emotional. A person may answer a question in many different ways that have only are slight variations in their content. To use a decision tree to express this way of thinking is very difficult since one quickly encounters exponential growth in complexity due to slight variations in answers people may give. These slight variations significantly impact how meaningful the answer is to the person, but the increased number of possible answers greatly complicates the decision tree. In addition the order in which questions are considered by a person can greatly impact the emotional response. If a decision tree needs to factor in all the possible permutations of how questions can be ordered one again faces a serious complexity barrier. When an issue is not concrete, such as would be the case with a societal or political issue, a more meaningful presentation of the issue to the person often requires the factoring of a person's particular beliefs, habits and prejudices. There are so many variations in these factors that a decision tree is ill equipped to deal with the number. A person finds information more meaningful when they are enabled to think in his/her own fashion at his/her own speed without the constant prodding to make decisions however small. The structure of a decision tree does not approximate this manner in which a user thinks and a new approach is required to map in a more agile and flexible way what really goes on in a person's mind. Further, Expert Systems are typically concerned only with the logic of a position not the person's emotional response to how the Expert Systems are presenting the position.